Dissecting the Kinetic Mechanism of Human Lysine Methyltransferase 2D and Its Interactions with the WRAD2 Complex

Human lysine methyltransferase 2D (hKMT2D) is an epigenetic writer catalyzing the methylation of histone 3 lysine 4. hKMT2D by itself has little catalytic activity and reaches full activation as part of the WRAD2 complex, additionally comprising binding partners WDR5, RbBP5, Ash2L, and DPY30. Here, a detailed mechanistic study of the hKMT2D SET domain and its WRAD2 interactions is described. We characterized the WRAD2 subcomplexes containing full-length components and the hKMT2D SET domain. By performing steady-state analysis as a function of WRAD2 concentration, we identified the inner stoichiometry and determined the binding affinities for complex formation. Ash2L and RbBP5 were identified as the binding partners critical for the full catalytic activity of the SET domain. Contrary to a previous report, product and dead-end inhibitor studies identified hKMT2D as a rapid equilibrium random Bi–Bi mechanism with EAP and EBQ dead-end complexes. Matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-ToF MS) analysis showed that hKMT2D uses a distributive mechanism and gives further insights into how the WRAD2 components affect mono-, di-, and trimethylation. We also conclude that the Win motif of hKMT2D is not essential in complex formation, unlike other hKMT2 proteins.


■ INTRODUCTION
Epigenetic control is mediated by enzymatic introduction or removal of covalent modifications to histone proteins or by directly modifying DNA and RNA through chromatin remodeling. Histones are small alkaline proteins with unstructured N-terminal tails that are prone to post-translational modifications (PTMs). 1−3 Said modifications include phosphorylation, methylation, acetylation, ubiquitination, and SUMOylation of residue side chains such as lysine, arginine, histidine, and serines also referred to as the "histone code." 4,5 The coordinated deposition, interpretation, and removal of these PTMs, required to achieve the correct biological effect, are profoundly complex, and the interplay between histone code readers and writers is still not completely understood. 6 Histone tail PTMs can confer control over gene transcription either directly through promoting binding of transcription factors or indirectly through mediating chromatin structure reorganization, altering DNA accessibility. 3,7,8 Human lysine methyltransferases (hKMTs) are a superfamily that can be divided into five classes which transfer methyl groups from the methyl donor S-adenosyl-L-methionine (AdoMet) to the ε-amino group of lysines, producing Sadenosyl-L-homocysteine (AdoHcy) as a byproduct. 5,9−11 In mammalian cells, methylation of DNA, histones, and other proteins is as common as phosphorylation and ubiquitination. 12 Unlike other PTMs that are mainly recognized by charge or size differences, such as phosphorylation and ubiquitination, respectively, the addition of 1, 2, or 3 methyl groups does not alter the overall charge of the ε-amino group of lysine at neutral pH and only contributes a modest 14 Da to the overall protein. 13 Lysine side chains are commonly involved in salt bridge or hydrogen bond formation; however, as the methylation state of a lysine side chain increases, the hydrogen bond potential decreases. Conversely, the addition of a methyl can create an unconventional CH−O hydrogen bond; 14−17 therefore, effector proteins that recognize different intermediate states of methyllysine must be fine-tuned to discriminate between different methylation states.
In humans, class I and V methyltransferases act on histones and differ, respectively, by the absence or presence of a catalytic SET domain. 11 The SET (SU(var), Enhancer of Zeste and Trithorax) domain is formed by ∼140 residues, highly conserved in its sequence, and present in all studied eukaryotes. 1,10,18 The class V methyltransferases are further subdivided into seven known SET families: SUV3/9, SET1, SET2, EZ, RIZ, SMYD, and SUV4−20. 10 Common with many proteins involved in epigenetic control, the SET1/MLL/KMT2 family of methyltransferases is of therapeutic interest as dysregulation or mutation has been found to be involved in various cancers, frequently with mutations located in the catalytic SET domain. 19−22 The KMT2 methyltransferases are again divided into subgroups based on their sequence homology and methylation activity. 1,23 hKMT2A/B (MLL1/2) show homology with Drosophila melanogaster trithorax (Trx) and primarily regulate Hox genes through trimethylation, whereas hKMT2F/G (MLL5/ 6 or SET1A/B) trimethylate at promoter regions and show homology to the Set1 protein of both Saccharomyces cerevisiae and D. melanogaster. hKMT2C/D (MLL3/4) share their sequence homology with D. melanogaster trithorax-related protein (Trr) and preferentially monomethylate enhancer regions of actively transcribed genes. 19,24−27 Monomethylation at enhancer regions is implicated in the accessibility and activation of these regions, and methylation performed by hKMT2D has been observed as necessary for recruitment and activation of FOXA1, PBX1, and ER α TF to specific chromatin sites. 19,27,28 hKMT2s are large proteins ranging from 1707 to 5537 residues, with the isolated proteins having little activity unless associated with the WRAD2 complex. 1,2,19,29,30 The WRAD2 complex consists of four proteins, WDR5 (WD repeat domain), RbBP5 (retinoblastoma-binding protein), ASH2L (absent small or homoeotic 2-like), and homodimer DPY30 (Dumpy-30). 2,31 It is thought that forming the hKMT2:WRAD2 complex alters the active site conformation, allowing optimum alignment of the methyl donor and acceptor for an efficient S N 2 reaction. 32 The WDR5 interacting motif (Win motif) of the Win-SET domain is also thought to be essential in WRAD2 complex formation in hKMT2 enzymes and is driven by the critical initial formation of the Win−WDR5 interaction via a conserved Win motif arginine residue. 29,32,33 Given the size of these proteins, hKMT2D is the largest of the family at 5537 amino acids, 1 and most in vitro studies have used truncated constructs focusing on the Win-SET region for both functional and structural studies. 29,32−35 Understanding an enzyme's catalytic mechanism is important, as during the catalytic cycle, the enzyme presents numerous intermediates through the binding of substrates and formation of products. 36 A number of publications have reported hKMT2D kinetic parameters and the effect of the WRAD2 complex on catalysis, but few have performed full mechanistic analysis, with one group reporting a sequential Bi−Bi mechanism. 37 Here, we expressed the hKMT2D SET domain and the individual WRAD2 proteins. Measurement of the steady state and product and dead-end inhibitor parameters identifies the hKMT2D mechanism as a rapid equilibrium random Bi−Bi mechanism with EAP and EBQ dead-end complexes. Monitoring products over time with matrix-assisted laser desorption ionization time-of-flight (MALDI-ToF) mass spectrometry shows that hKMT2D uses a distributive enzyme mechanism with monomethylation being the most efficient reaction. Furthermore, we identify the key interactions of the WRAD2 complex and a minimal complex that processes activity that is equivalent to that of the full WRAD2 complex. 60 size exclusion column equilibrated with dialysis buffer. Complexes were reconstituted by incubating equimolar amounts of required proteins on ice for 1 h, and complexes were separated using a Superdex 200 26/60 size exclusion column equilibrated with dialysis buffer. Peak fractions were concentrated to approximately 20 mg/mL, flash-frozen in liquid nitrogen, and stored at −80°C. Intact mass spectrometry was performed using a Sciex X500B Q-TOF with Sciex Excion LC instrument and a bioZen 3.6 μm Intact XB-C8 column. All proteins were diluted at least 10× in mass spec buffer (5% acetonitrile, 0.1% formic acid) to 0.1 mg/mL.
Methyltransferase Luminescence Assay. SET domain activity was monitored with a quantitative endpoint assay determining AdoHcy production using MTase-Glo by Promega. 39 Assays were performed as time courses at room temperature with buffer constituents, 50 mM Tris, pH 7.5, 50 mM NaCl, 1 mM DTT, 1% DMSO, and 0.005% w/v Triton X-100 in deionized water. The SET domain was incubated with substrates AdoMet and H3 1−21 peptide, in a final volume of 4 μL. Addition of 1 μL of 0.5% v/v TFA was used to stop the methylation reaction at defined time points; 1 μL of 6× concentrated MTase-Glo reagent was added to each well and incubated at room temperature. After 30 min, 6 μL of the prefiltered MTase-Glo detection reagent was added and incubated for a further 30 min at room temperature. Luminescence was measured using an Envision 2101 Multilabel plate reader, and product concentrations were calculated using an AdoHcy standard curve. Steady-state rates were obtained by plotting AdoHcy production over time and normalized to SET domain concentration. Experiments were performed in triplicate and expressed as the mean ± SD. Data were analyzed using nonlinear regression in GraphPad Prism v9.1.
Steady-State Studies. Steady-state rates were measured in substrate matrix experiments. Data were globally fitted to ternary Bi−Bi and Ping−Pong models to obtain k cat and K M parameters (eqs 1 and 2).
The ternary Bi−Bi model The Ping−Pong model ) where ν is the initial rate, V max is the maximum velocity, [A] is the concentration of the varied substrate, [B] the concentration of the fixed substrate, K M A and K M B are the Michaelis constants of the varied and fixed substrates, respectively, and K d is the dissociation constant of the varied substrate. A detailed WRAD2 titration was performed using SET, WRAD2, AdoMet, and H3 1−21 peptide concentrations described in Figure S3 and fitted to eq 1.
The change in the catalytic parameters as a function of WRAD2 concentration was fitted to eq 3 where ρ obs is the observed value of either k cat , 1/K M , or k cat /K M , ρ max is the maximal value of either is the maximal value of the reciprocal of the dissociation constant for H3 1−21 , [WRAD2] is the concentration of the WRAD2 complex, and C is the background measurement.   5) where ν is the initial rate, V max is the maximum velocity, [S] is the concentration of the varied substrate, and K M is the Michaelis constant. The minimal complex matrix experiment using 20 nM SET/Ash2L/RbBP5 in a 1:1:1 ratio used a truncated Ash2L peptide (380−496-ISGRGS-539−598) and a 34 mer RbBP5 peptide (330−363) with 5, 10, 15, 20, 30, and 40 min time points. Substrate ranges for AdoMet and H3 1−21 were 0−50 and 0−400 μM, respectively, and fitted to eq 1.
Dead-End and Product Inhibitor Studies. AdoHcy and the trimethylated Me3H3 1−21 peptide were used as product inhibitors, while dead-end substrate analogues were sinefungin and NleH3 1−21 peptide. Steady-state rates were measured in substrate-inhibitor matrix experiments. Dead-end inhibitor experiments were performed with the second substrate fixed at K M , while product inhibitor experiments fixed the second substrate at K M or 20× K M . Assays using 20× K M AdoMet used a cofactor adjusted to pH 7.5. AdoHcy inhibition experiments used a maximum concentration of 8 μM with optimized MTase-Glo additions of 1 μL of 10× MTase-Glo reagent and 12 μL of the MTase-Glo detection reagent, and reactions were monitored over 30 Noncompetitive inhibition Mixed inhibition ) where ν is the initial rate, V max is the maximum velocity of the uninhibited reaction, [S] is the substrate concentration of the varied substrate, [I] is the inhibitor concentration, K M is the Michaelis−Menten constant, and K i is the inhibition constant.
To resolve any ambiguities in assigning inhibition type, the mixed inhibition model was used (eq 9), to derive the value of α, which is a measure of competitive or uncompetitive nature.
where [P] is the concentration of the product, ∑Pi is the sum of the product peak integrals, ∑Si is the sum of the substrate peak integrals, and [S 0 ] is the starting concentration of the substrate. Progress curves were fitted to sequential methylation models for two or three methylations using KinTek Explorer v10. 40 where Me0, Me1, Me2, and Me3 correspond to non-, mono-, di-, and trimethylated H3 1−21 peptides, respectively. SPR Binding Assays. Surface plasmon resonance experiments were performed using a T200 instrument (Cytiva) equipped with a research-grade NTA sensor S chip (Cytiva) at 20°C. For immobilization, the instrument was primed with a buffer composed of 10 mM HEPES, pH 7.5, 150 mM NaCl, 0.5 mM TCEP, and 0.05% Tween 20. The NTA chip was conditioned with three 2 min injections of 50 mM NaOH/1 M NaCl and a 2 min injection of 250 mM EDTA. Protein and reference flow cells were then prepared by a 2 min injection of 1 mM NiCl 2 and a 7 min injection of 0.2 M EDC/ 0.05 M NHS. Immediately, the protein (500 nM His-SET (5382− 5537) + ASH2L (380-496-ISGRGS-539-598) + RbBP5 peptide in immobilization buffer) was injected over the measurement flow cell to the desired RU level, followed by deactivation with a 7 min injection of 1 M ethanolamine pH 8 of all flow cells. For binding measurements, the system was then primed in a running buffer consisting of 50 mM Tris pH 7.5, 150 mM NaCl, 1 mM TCEP, 1−2% DMSO, and 0.05% Tween 20. Steady-state affinity data were recorded with NleH3 1−21 and AdoMet prepared in running buffer and injected at a flow rate of 30 μL/min in a concentrationdependent manner over both protein and reference cells and recorded at 10 Hz. Data processing included solvent correction and blank subtraction. The steady-state data were analyzed using Biaevaluation/Insight Software 1.1 (GE Healthcare/ Cytiva) using an implemented 1:1 interaction model.

Protein Expression and Intact Mass Spectrometry.
hKMT2D Win-SET was expressed in Sf21 cells, while WRAD2 proteins were expressed in E. coli and purified to homogeneity using column chromatography. Individual proteins were subjected to intact mass spectrometry to confirm the correct molecular mass. A list of amino acid sequences and tags can be seen in the Supporting Information ( Figure S1). Intact mass spectrometry of the Win-SET protein showed a smaller than expected mass of 20,794 Da, differing from the expected mass of 29,266 Da ( Figure S2). A loss of 8472 Da corresponds to the loss of the N-terminal 6×His tag, TEV cleavage site, and amino acids 5308 to 5361 including the Win motif. The conserved arginine at amino acid position 5340 is also within this cleaved region, a residue thought to be essential in Win-SET complex formation with WRAD2. 33 It is most likely that the cleavage occurs after purification, as the initial purification step uses nickel column affinity. Efforts to express a nontruncated form of the Win-SET domain, by making point mutations around the amino acid 5360 cleavage site, were unsuccessful (data not shown). As the Win motif has been lost due to proteolysis, we shall refer to the catalytic subunit expressed here as the SET domain. Biochemistry pubs.acs.org/biochemistry Article WRAD2 Titration. To assess the effect of the WRAD2 complex on the catalytic parameters of the SET domain, a detailed WRAD2 titration was carried out using substrate matrix experiments by varying one substrate at a range of fixed concentrations of the second substrate. The data were globally fitted to eq 1 using nonlinear regression to determine k cat , K M   (Table 1). A 30-fold increase in H3 1−21 affinity and a 10-fold increase in k cat were observed with increasing WRAD2 concentration. No significant change in AdoMet K M was observed throughout the range of the titration, showing that WRAD2 has no effect on AdoMet binding when forming the ternary complex. Interestingly, the calculated K d values for both AdoMet and H3 1−21 , which represent substrate binding to the free enzyme, decreased with increasing WRAD2 concentration. For both substrates, K M and K d had equivalent values in the absence of WRAD2, but the calculated K d values reduced 10-fold at the highest WRAD2 concentration of 125 nM. Plotting the catalytic parameters as a function of WRAD2 concentration can give insights into the affinity and stoichiometry of any interactions with the SET domain and how WRAD2 affects the catalytic rate, substrate binding, and catalytic efficiency ( Figure  1).
Data were fitted to a modified Hill model (eq 3), which is a cooperative model that reports a single K d value, where multiple interactions can have similar affinities. The model also contains constant C, which allows for the basal activity of the SET domain in the absence of the WRAD2 complex. A plot of k cat versus WRAD2 concentration showed a sigmoidal curve with a K d = 12.2 ± 1.2 nM with a gradient of 2.2 ± 0.4. This WRAD2 dependence can only be explained by the involvement of at least two protein interactions. H3 1−21 affinity plotted as 1/K M showed a sigmoidal curve with a K d = 4.1 ± 0.3 nM with a Hill slope = 2.6 ± 0.2. In addition, the overall rate constant, k cat /K M , was also plotted and again presented a sigmoidal curve with a K d = 11.2 ± 1.0 nM with a Hill slope = 2.7 ± 0.3. Hill slopes of 2.6 and 2.7 for 1/K M and k cat /K M , respectively, indicate the participation of two to three protein interactions. Attempts to fit the k cat , 1/K M , and k cat /K M data sets to various models of independent or combinations of independent and cooperative binding yielded poor fits (data not shown). This is most likely due to the dissociation constants being too close in magnitude for the models to distinguish. AdoMet K M showed no change with increasing WRAD2 concentration, so provided no information on the WRAD2 interaction. In addition, 1/K d for H3 1−21 was fitted to a cooperative three-binding-site model, where the Hill slope was set to a value of 3 (eq 4), giving a K d of 2.5 ± 0.6 nM ( Figure S4). This K d is not significantly different from the K d of 4.1 nM identified from the 1/K M fit so may be the result of the same protein interactions. Again, attempts to fit these data to the modified Hill equation (eq 3) or various models of multiple binding sites were unsuccessful, but the cooperative three-binding-site model is consistent with Hill slopes observed from the k cat , 1/K M , and k cat /K M fits. It should be noted that all of the measured interactions are well below the theoretical tight-binding limit of the assay, indicating that the active fraction of the SET domain must be below 4% of the total enzyme concentration. Individual substrate matrix global fits can be seen in Figure S5.  Effect of Individual WRAD2 Components on SET Activity. WDR5, RbBP5, Ash2L, and DPY30 were tested individually and in combination to identify the key WRAD2 components that interact with the SET domain ( Figure 2).
As the K M for AdoMet appeared to be agnostic to WRAD2 concentration, experiments were performed at a fixed AdoMet concentration of 5 μM while varying the H3 1−21 concentration. Only Ash2L was identified to enhance peptide affinity 2-fold in isolation, whose effect was amplified 30-fold in the presence of RbBP5. No WRAD2 component in isolation had any stimulatory effect on k cat , but RbBP5 in combination with Ash2L enhanced k cat to a level equivalent to the complete WRAD2 complex. These data showed that Ash2L and RbBP5 form two key interactions with the SET domain that affect both H3 1−21 affinity and stimulation of k cat .
SET/Ash2L/RbBP5 Minimal Complex. To investigate the minimal requirement of the Ash2L and RbBP5 interactions with the SET domain for efficient activity, a substrate matrix experiment was performed with the SET/Ash2L/RbBP5 complex using a truncated Ash2L construct (380-496-ISGRGS-539-598) and a 34 mer RbBP5 (330−363) peptide and fitted to eq 1. k cat was ∼2-fold reduced and AdoMet K M was equivalent to that measured with the full WRAD2 complex, but H3 1−21 K M was 97.50 ± 3.1 μM cf. 16.5 ± 1.25 μM (Table 2).
These data confirm that this minimal complex shows the importance of the Ash2L and RbBP5 interactions, even in their truncated forms. Substrate matrix fits can be seen in Figure S6.
Mono-and Dimethylated Peptide Substrates. As the KMT2D SET domain can catalyze mono-, di-, and trimethylation of H3K4, Me1H3 1−21 and Me2H3 1−21 peptides were used in substrate matrix experiments to determine substrate specificity (Table 2 and Figure S7). Using Nucleosome Substrates. To investigate any changes in substrate specificity under a potentially more physiologically relevant setting, recombinant mononucleosomes were used. Using recombinant mononucleosomes has the advantage of being able to control methyl marks on any given histone at any given position. These mononucleosomes provided substrates with specific methyl marks on the H3.1K4 residue. Overall, all nucleosome substrates were more efficient substrates than the peptides tested (Table 2 and Figure S8). Nevertheless, mononucleosomes followed a similar trend as to that observed for peptide substrates, with the catalytic efficiency decreasing with each methylation reaction from 2.0 × 10 4 to 1.5 × 10 3 and 2.6 × 10 2 M −1 s −1 for mono-, di-, and trimethylation, respectively. These data suggest that whether methylating peptides or nucleosome substrates, the monomethylation reaction is the most efficient. The kinetic analysis of HeLa oligonucleosomes as a substrate gave a catalytic efficiency of 1.2 × 10 4 M −1 s −1 . The catalytic efficiency of the oligonucleosomes was in good agreement with the recombinant mononucleosomes. The reduction of efficiency relative to the unmethylated recombinant mononucleosomes was expected, given the possibility of increased methylation of the HeLa-derived oligonucleosomes at the H3K4 position. Caution should be taken while reporting the absolute values for k cat and K M from the nucleosome experiments, as the assays were limited by the concentration of the starting stocks, meaning that full titration curves could not always be measured.

MALDI-ToF MS Time Course.
To investigate the distributive or processive nature of the SET domain reaction, MALDI-ToF mass spectroscopy was used to monitor the peptide methylation state as a function of time (Figures 3 and  S9 and Table 3).
In addition, these experiments can also provide insights into the effect of individual and combinations of WRAD2 proteins on product formation. Experiments used excess AdoMet at 200 μM, so the cofactor would not become limiting. Time courses in all conditions showed the consumption of the H3 1−21 substrate and the formation of Me1H3 1−21 . Only after 24 h did the SET, SET/Ash2L, and SET/RbBP5 conditions show a significant quantity of Me2H3 1−21 of ∼5 μM. The activity and product distribution significantly increased on the formation of the SET/Ash2L/RbBP5 complex, with the rapid consumption of H3 1−21 within 60 min. After a significant concentration of Me1H3 1−21 had accumulated, >75% of the total species, the evolution of Me2H3 1−21 was observed with the accompanied consumption of Me1H3 1−21 . The same trend was observed for the formation of Me3H3 1−21 , requiring substantial accumulation of Me2H3 1−21 before trimethylation would proceed. Fitting the progress curves in KinTek Explorer ( Figure S9) showed that dimethylation was ∼20-fold slower than the  Figure 2. Formation of higher complexes beyond SET/Ash2L/RbBP5 by the addition of WDR5 and DPY30 showed no measurable enhancement of activity or trimethylation. These data are also consistent with a distributive mechanism (Scheme 1), as a processive mechanism would show the consumption of H3 1−21 and formation of Me3H3 1−21 with little or no mono-or dimethylated product. Product Inhibitor Studies. As the substrate matrix experiments often cannot confidently identify the enzyme mechanism, product inhibition studies were performed, using adenosyl-homocysteine (AdoHcy) and trimethylated H3 peptide (Me3H3 1−21 ) as product inhibitors. Product inhibitors are part of the normal reaction coordinate and can bind to specific enzyme forms during the catalytic cycle. Me3H3 1−21 was shown to be a competitive inhibitor when H3 1−21 was the varied substrate and the concentration of AdoMet was fixed at both K M and 20× K M . To check the validity of fitting to a competitive model, the data were fitted to the mixed inhibition model (eq 9) to determine the α value (α). When α is >1, the data tend toward competitive inhibition, when α is <1, the data tend toward uncompetitive inhibition, and when α = 1, the data show no bias toward either competitive or uncompetitive inhibition and are consistent with noncompetitive inhibition. When AdoMet was fixed at K M and 20× K M , the α values for Me3H3 1−21 were>1000 from both fits, confirming competitive inhibition. Me3H3 1−21 showed noncompetitive inhibition when AdoMet was varied at fixed K M H3 1−21 , but this inhibition was abolished when the fixed concentration of the H3 1−21 peptide was increased to 20× K M (Table 4 and Figures  4 and S10).
AdoHcy inhibition was measured using concentrations up to a maximum concentration of 8 μM due to the limitations of the MTase-Glo technology. AdoHcy was a competitive inhibitor when varying AdoMet at both fixed K M and 20× K M H3 1−21 concentrations, with the values of α being >1000 and 6.8, respectively. Noncompetitive inhibition was observed when H3 1−21 was varied at K M AdoMet. This inhibition was abolished when AdoMet was increased to 20× K M ( Figure  S11). The competitive and noncompetitive product inhibition patterns observed are consistent with three enzyme mechanisms: Theorell−Chance, Ping−Pong, and rapid equilibrium random Bi−Bi with dead-end EAP and EBQ complexes. 41 Dead-End Inhibitor Studies. To further study the SET domain mechanism ascertained from the product inhibitor studies, dead-end inhibitors sinefungin and lysine 4 to the norleucine H3 1−21 peptide (NleH3 1−21 ) were used. In comparison to product inhibitors, dead-end inhibitors act as substrate analogues and divert the enzyme off the normal reaction coordinate. Hydrophobic mutations of H3K9, K27, and K36, by leucine, isoleucine, and methionine, have been reported to inhibit a number of KMTs and form the rational basis for using an inhibitory norleucine peptide. 42−44 All experiments using dead-end inhibitors were performed with the nonvaried substrate concentration fixed at K M . Sinefungin and NleH3 1−21 were fitted to a competitive model when AdoMet and H3 1−21 were varied, respectively. The α values for sinefungin and NleH3 1−21 were 21.1 and 862.2, respectively, confirming competitive inhibition. Unexpectedly, uncompetitive inhibition was observed for sinefungin and NleH3 1−21 when H3 1−21 and AdoMet were varied, respectively, and fitted to an uncompetitive model (Table 4 and Figures S12 and S13). Again, these data were fitted with a mixed inhibition model, which showed α values <1 of 0.16 and 0.09, confirming uncompetitive inhibition. SPR Binding Assays. To investigate the AdoMet and peptide binding properties of the SET domain, direct binding assays were performed using SPR. The minimal complex was used, as it gave better quality data due to its smaller size compared to the full SET/WRAD2 complex. AdoMet was found to bind the SET domain with an affinity of 9 ± 2 μM. This was in line with the K M values measured during the steady-state experiments and gave confidence that the SET domain had not been adversely affected by immobilization. Peptide binding was measured both in the presence and absence of AdoMet using the dead-end inhibitor NleH3 1−21 peptide. In the absence and presence of AdoMet, NleH3 1−21 bound with affinities of 160 ± 57 and 10 ± 3 μM, respectively    Parameters calculated from C = competitive, NC = noncompetitive, and UC = uncompetitive inhibition models using the Cleland nomenclature.  Biochemistry pubs.acs.org/biochemistry Article motif), and the individual full-length WRAD2 components. Subsequently, intact mass spectrometry of the Win-SET domain revealed that the N-terminal Win motif was missing from the purified protein. The Win motif contains the conserved Arg5340 residue, which in multiple studies with KMT2A is proposed to form a central interaction with WDR5 and central to complex formation. 33 Unsuccessful attempts were made to produce the intact Win-SET protein, including introducing point mutations around the cleavage site to inhibit proteolysis. Being unable to produce the intact Win-SET protein made performing a comparison study between SET and Win-SET domains impossible but would still provide information on the absolute requirement of the Win motif for WRAD2 modulation. Due to the absence of the Win motif, we refer to the catalytic subunit used here only as the SET domain.
To investigate the SET/WRAD2 interaction, SET domain kinetic parameters were measured at several WRAD2 concentrations, showing that the WRAD2 complex has a profound effect on the catalysis and binding of H3 1−21 . Fitting of k cat , 1/K M , and k cat /K M as a function of WRAD2 concentration to the Hill equation returned gradients ranging from 2.2 to 2.7. This suggests that there are at least two interactions that elicit the enhanced response in H3 1−21 affinity and catalytic activity, with measured affinities of ∼4 and 12 nM. The modulation of catalytic parameters is not the result of a single component of the WRAD2 complex, but the synergistic effect of multiple interactions, as illustrated by the Hill slopes >2. A single interaction modulating catalysis or substrate binding would have resulted in a Hill slope of near 1. Assays performed with the SET domain with individual and combinations of WDR5, RbBP5, Ash2L, and DPY30 identified Ash2L and RbBP5 as the two key proteins that together restore the SET domain function to that of the full WRAD2 complex. As Ash2L was observed to increase H3 1−21 affinity in isolation and H3 1−21 affinity responds to WRAD2 concentrations from 0.5 nM and above, we therefore assigned Ash2L a K d of 4 nM. Using a similar process, we can assign RbBP5 a K d of 12 nM, as the stimulation of k cat does not occur until WRAD2 reaches a concentration of ∼2 nM and above. This highly active trimeric complex of SET/Ash2L/RbBP5 is consistent with observations from other studies. 45−48 The relevance of this finding was also demonstrated in a substrate matrix experiment using the SET/ Ash2L/RbBP5 minimal complex, consisting of a truncated Ash2L peptide (residues 539-496-ISGRGS-539-598) and an RbBP5 peptide (residues 330−363), based on the KMT2C study by Li et al. 45 k cat was only 2-fold lower and the K M value was 6-fold larger than those of the full SET/WRAD2 complex. The Win motif/WDR5 interaction is proposed to be the hub of complex formation in KMT2 proteins, mainly from studies conducted with KMT2A; but data presented here for the hKMT2D SET domain show that complexes can be formed in the absence of the Win motif. Cryo-electron microscopy has shown that the WRAD2 complex is dynamic in nature so can conceivably dissociate in solution at low concentrations and not titrate as a single entity. 35 This structural information also formed the basis of the assumption that the SET domain associates with equimolar amounts of each of the WRAD2 components in solution. As all of the measured WRAD2 interactions are well below the tight-binding limit of the assay, this shows that the fraction of active enzyme is below 4%; therefore, the reported k cat values in this study will be greatly underestimated. Without a tight-binding ligand, we cannot accurately measure the active fraction of enzyme in solution, although our values are in line with those previously reported by Zhang et al. 32 A processive or distributive mechanism of the hKMT2D SET domain was investigated using MALDI-ToF mass spectrometry by monitoring the peptide substrate and product distributions as a function of time. This revealed a distributive mechanism, where the H3 peptide is monomethylated and released into solution before rebinding to carry out the second methylation reaction. This process is repeated to generate the trimethylated species. The release and rebinding of the methylated product must allow the reorientation of the lysine side chain to facilitate the second and third methylations (Scheme 1). The distributive mechanism is consistent with the kinetic models used in this study and also indicates that during initial rate experiments, the monomethylated peptide is the predominant form in solution. MALDI-ToF MS time course data and substrate matrix experiments using H3 1−21 , Me1H3 1−21 , and Me2H3 1−21 peptides showed that the rate of each methylation reaction decreased ∼20and 10-fold for each methylation step, respectively. We would postulate that a WRAD2 titration with Me1 and Me2H3 1−21 substrates would show similar trends in the measured k cat and K M values as those with the H3 1−21 substrate. This hypothesis is supported by the observation that all methylation reactions are stimulated by the formation of the SET/Ash2L/RbBP5 complex in the MALDI-ToF experiments. Nucleosomes proved to be the most efficient substrate in all methylation states compared to peptide substrates, driven much by the reduced substrate K M s, but mononucleosomes also followed a similar decline in catalytic efficiency upon methylation. This makes the hKMT2D SET domain an efficient monomethylase in in vitro. HeLa oligonucleosomes had a similar catalytic efficiency to the unmethylated recombinant mononucleosomes, indicating that the samples used were predominantly free of methylation at the H3 lysine 4 position. MALDI-ToF MS also reinforced the significance of the SET/Ash2L/RbBP5 complex, as WDR5 and DPY30 do not further enhance enzyme activity or methylation efficiency. It is unclear whether this is due to the absent Win motif denying WDR5 and DPY30 critical interactions but is consistent with the observations by Li et al. 45 It is important to note that using the biochemical techniques described here can only identify interactions that alter the SET domain catalytic parameters but cannot report on potentially critical binding partners that act solely as scaffolds for protein−protein or protein−DNA interactions in vivo. A notable observation is how the hKMT2D SET domain shows remarkable similarity to wild-type EZH2, the catalytic KMT subunit of the PRC2 complex, in terms of the measured catalytic parameters from mono-to trimethylation, and its distributive mechanism. 49 Steady-state studies could not identify the enzyme mechanism solely from substrate matrix experiments. This is reflected here as the favored model changes, in a WRAD2 concentration-dependent manner, from ternary to a Ping− Pong model. This was the result of the calculated values for substrate K d reducing with increasing WRAD2 concentration. When K d becomes significantly small, then the K d K M B term of the ternary complex equation (eq 1) tends to zero, and the equation collapses down to form the Ping−Pong model (eq 2). It is unlikely that an enzyme mechanism will change from the one that forms a ternary complex to the one that forms a covalent intermediate. Therefore, the ternary complex model satisfies all of the observed steady-state data. Moreover, a Biochemistry pubs.acs.org/biochemistry Article Ping−Pong mechanism would have suggested that the hKMT2D SET domain uses a novel mechanism among KMTs, with no published examples to date. To further probe the true enzyme mechanism, product and dead-end inhibitor studies were performed and the inhibition patterns were analyzed. The inhibition patterns can either be compared to published tables or be derived from first principles using Cleland's rules to identify the enzyme mechanism. 41,50,51 Published tables would indicate that the competitive and noncompetitive product inhibition patterns are consistent with the Theorell−Chance mechanism, Ping−Pong mechanism, and rapid equilibrium random Bi−Bi mechanism with deadend EAP and EBQ complexes. Dead-end inhibitors produced distinctive competitive and uncompetitive patterns consistent with the Ping−Pong mechanism, which was surprising, as SET domain catalysis is widely accepted to occur through the nucleophilic attack of the AdoMet sulphonium center by the εamino group of lysine. 32 For the Ping−Pong mechanism to hold, the product inhibitors Me3H3 1−21 and AdoHcy cannot be competitive with their cognate substrates but would present as noncompetitive inhibition ( Figure S15). This observation rules out Ping−Pong and Theorell−Chance as possible mechanisms. Although at first glance, the three possible mechanisms share the same product inhibition patterns, incorrect assignment of the product P and Q notation can have a profound effect on identifying the correct mechanism. 41,50 In this instance, product inhibitors were sufficient to determine the SET domain mechanism. Uncompetitive inhibition has previously been observed with dead-end inhibitors with other SET domains, stating the formation of the E:AdoMet complex is a prerequisite for norleucine mimetics while not being required for lysine substrate binding. 42,52,53 With this in mind, we suggest that the hKMT2D SET domain uses a rapid equilibrium random Bi− Bi mechanism with dead-end EAP and EBQ complexes (Scheme 2). Furthermore, dead-end EAP and EBQ complexes are consistent with products Me3H3 1−21 and AdoHcy competing with their cognate substrates. The potential dead-end complexes formed by the SET domain in Scheme 2 are made more complex by the fact that there are potentially three methylation events and therefore three products. The EAP and EBQ dead-end complexes in this case refer to E:H3 1−21 : AdoHcy and E:AdoMet:MeH3 1−21 , respectively, where MeH3 1−21 can be the mono-, di-, or trimethylated peptide. An inhibitory EBQ complex arising from Me1H3 1−21 or Me2H3 1−21 would require binding in a specific orientation with the methyl group directed toward the catalytic site, otherwise a further methylation reaction will occur. We therefore propose that this inhibitory conformation is already satisfied when Me1H3 1−21 or Me2H3 1−21 remains bound to the enzyme after the methylation reaction and AdoHcy release. Therefore, if AdoMet binds before Me1H3 1−21 or Me2H3 1−21 is released, the E:AdoMet:Me1H3 1−21 or E:AdoMet:Me2H3 1−21 dead-end complex is formed. This is also consistent with the distributive mechanism reported here and a mechanism supported by Wang et al for PRMT5. 54 A paper published by Zheng et al. proposes that the hKMT2D minimal complex uses a sequential Bi−Bi mechanism, where AdoMet is required to bind first. 37 We believe that this discrepancy could in part be explained by the use of a slow substrate rather than a true product inhibitor. Zheng et al. used Me1H3 1−20 as a product inhibitor, but we show that both Me1H3 1−21 and Me2H3 1−21 are substrates for SET/WRAD2. If the minimal complex can use Me1H3 1−20 as a substrate, then the data, depending on the catalytic efficiency, can be skewed toward weak non-or uncompetitive inhibition. Indeed we have collected MALDI-ToF data with the minimal complex showing the evolution of Me2 and Me3H3 1−21 products (data not shown). Performing the product inhibition experiments at both K M and saturating fixed substrate concentrations would have been useful to resolve any ambiguity, as saturating H3 1−20 would abolish Me1H3 1−20 inhibition in a random mechanism. SPR data collected by ourselves showed that the NleH3 1−21 peptide does indeed bind to the SET minimal complex, but with greater affinity in the presence of AdoMet, thus not only ruling out a random mechanism but also showing a disconnect between steady-state and direct binding assays using dead-end inhibitors. Conversely, we cannot rule out that the hKMT2D minimal complex uses a different mechanism to the full SET/WRAD2 complex.  In summary, there are two critical WRAD2 components, Ash2L and RbBP5, both with low nanomolar affinities for the hKMT2D SET domain that modulate catalytic activity and substrate affinity. The Win motif is not crucial for SET/ WRAD2 complex formation. Finally, the hKMT2D SET domain uses a rapid equilibrium Bi−Bi mechanism with EAP and EBQ dead-end complexes. It is hoped that this greater mechanistic insight into hKMT2D can help guide drug discovery strategies. The knowledge of the possible enzyme forms available during the catalytic cycle and the involvement of the key protein−protein interactions enable the rational design of assays to target defined enzyme complexes by smallmolecule inhibitors. ■ ASSOCIATED CONTENT
Amino acid sequences of human KMT2D and WRAD2 complex components; WRAD2 titration experimental conditions; intact mass spec of the Win-SET protein; steady-state plots and fits to the ternary complex and Michaelis−Menten models; MALDI-ToF MS time courses; AdoHcy and Me3H3 1−21 product inhibitor plots and fits; sinefungin and NleH3 1−21 dead-end inhibitor plots and fits; and hypothetical hKMT2D Ping−Pong mechanism (PDF)